pH

import warnings
warnings.filterwarnings("ignore")

import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.plotting import plot_base_map, plot_map_subplots
from ind_setup.core import fontsize

sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Load Data

Superficial depth

data_xr = xr.open_dataset(op.join(os.getcwd(), '..', '..','..', 'data/data_phyc_o2_ph.nc')).isel(depth = 0)
dataset_id = 'ph'
label = 'pH'

data_xr

<xarray.Dataset> Size: 5MB
Dimensions:    (latitude: 40, longitude: 31, time: 360)
Coordinates:
    depth      float32 4B 0.5058
  * latitude   (latitude) float32 160B 1.75 2.0 2.25 2.5 ... 11.0 11.25 11.5
  * longitude  (longitude) float32 124B 129.5 129.8 130.0 ... 136.5 136.8 137.0
  * time       (time) datetime64[ns] 3kB 1993-01-01 1993-02-01 ... 2022-12-01
Data variables:
    phyc       (time, latitude, longitude) float32 2MB ...
    o2         (time, latitude, longitude) float32 2MB ...
    ph         (time, latitude, longitude) float32 2MB ...
Attributes:
    contact:                   servicedesk.cmems@mercator-ocean.eu
    Conventions:               CF-1.6
    title:                     Monthly mean fields for product GLOBAL_REANALY...
    credit:                    E.U. Copernicus Marine Service Information (CM...
    institution:               Mercator Ocean
    producer:                  CMEMS - Global Monitoring and Forecasting Centre
    source:                    MERCATOR FREEBIORYS2V4
    references:                http://marine.copernicus.eu
    copernicusmarine_version:  2.0.0a4

xarray.Dataset

• Dimensions:
  • latitude: 40
  • longitude: 31
  • time: 360
• Coordinates: (4)
  • depth
    ()
    float32
    0.5058

    units :

    m

    positive :

    down

    unit_long :

    Meters

    standard_name :

    depth

    axis :

    Z

    long_name :

    Depth

    array(0.50576, dtype=float32)

  • latitude
    (latitude)
    float32
    1.75 2.0 2.25 ... 11.0 11.25 11.5

    units :

    degrees_north

    unit_long :

    Degrees North

    standard_name :

    latitude

    axis :

    Y

    long_name :

    Latitude

    array([ 1.75,  2.  ,  2.25,  2.5 ,  2.75,  3.  ,  3.25,  3.5 ,  3.75,  4.  ,
            4.25,  4.5 ,  4.75,  5.  ,  5.25,  5.5 ,  5.75,  6.  ,  6.25,  6.5 ,
            6.75,  7.  ,  7.25,  7.5 ,  7.75,  8.  ,  8.25,  8.5 ,  8.75,  9.  ,
            9.25,  9.5 ,  9.75, 10.  , 10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 ],
          dtype=float32)

  • longitude
    (longitude)
    float32
    129.5 129.8 130.0 ... 136.8 137.0

    units :

    degrees_east

    unit_long :

    Degrees East

    standard_name :

    longitude

    axis :

    X

    long_name :

    Longitude

    array([129.5 , 129.75, 130.  , 130.25, 130.5 , 130.75, 131.  , 131.25, 131.5 ,
           131.75, 132.  , 132.25, 132.5 , 132.75, 133.  , 133.25, 133.5 , 133.75,
           134.  , 134.25, 134.5 , 134.75, 135.  , 135.25, 135.5 , 135.75, 136.  ,
           136.25, 136.5 , 136.75, 137.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1993-01-01 ... 2022-12-01

    long_name :

    Time

    axis :

    T

    standard_name :

    time

    unit_long :

    Hours Since 1950-01-01

    array(['1993-01-01T00:00:00.000000000', '1993-02-01T00:00:00.000000000',
           '1993-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          shape=(360,), dtype='datetime64[ns]')

• Data variables: (3)
  • phyc
    (time, latitude, longitude)
    float32
    ...

    units :

    mmol m-3

    unit_long :

    millimoles per cubic meter

    standard_name :

    mole_concentration_of_phytoplankton_expressed_as_carbon_in_sea_water

    valid_min :

    0.0120573435

    valid_max :

    42.421917

    long_name :

    Total Phytoplankton

    [446400 values with dtype=float32]

  • o2
    (time, latitude, longitude)
    float32
    ...

    units :

    mmol m-3

    unit_long :

    millimoles of Oxygen per cubic meter

    standard_name :

    mole_concentration_of_dissolved_molecular_oxygen_in_sea_water

    valid_min :

    0.27477467

    valid_max :

    465.18527

    long_name :

    Dissolved Oxygen

    [446400 values with dtype=float32]

  • ph
    (time, latitude, longitude)
    float32
    ...

    units :

    1

    unit_long :

    1

    standard_name :

    sea_water_ph_reported_on_total_scale

    valid_min :

    5.892493

    valid_max :

    8.417508

    long_name :

    PH

    [446400 values with dtype=float32]

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 1.75,   2.0,  2.25,   2.5,  2.75,   3.0,  3.25,   3.5,  3.75,   4.0,
            4.25,   4.5,  4.75,   5.0,  5.25,   5.5,  5.75,   6.0,  6.25,   6.5,
            6.75,   7.0,  7.25,   7.5,  7.75,   8.0,  8.25,   8.5,  8.75,   9.0,
            9.25,   9.5,  9.75,  10.0, 10.25,  10.5, 10.75,  11.0, 11.25,  11.5],
          dtype='float32', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([ 129.5, 129.75,  130.0, 130.25,  130.5, 130.75,  131.0, 131.25,  131.5,
           131.75,  132.0, 132.25,  132.5, 132.75,  133.0, 133.25,  133.5, 133.75,
            134.0, 134.25,  134.5, 134.75,  135.0, 135.25,  135.5, 135.75,  136.0,
           136.25,  136.5, 136.75,  137.0],
          dtype='float32', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1993-01-01', '1993-02-01', '1993-03-01', '1993-04-01',
                   '1993-05-01', '1993-06-01', '1993-07-01', '1993-08-01',
                   '1993-09-01', '1993-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=360, freq=None))

• Attributes: (9)

  contact :

  servicedesk.cmems@mercator-ocean.eu

  Conventions :

  CF-1.6

  title :

  Monthly mean fields for product GLOBAL_REANALYSIS_BIO_001_029

  credit :

  E.U. Copernicus Marine Service Information (CMEMS)

  institution :

  Mercator Ocean

  producer :

  CMEMS - Global Monitoring and Forecasting Centre

  source :

  MERCATOR FREEBIORYS2V4

  references :

  http://marine.copernicus.eu

  copernicusmarine_version :

  2.0.0a4

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'magma_r', 
                vmin = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label= label)

<matplotlib.colorbar.Colorbar at 0x17b49be30>

data_y = data_xr.resample(time='1YE').mean()
im = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'magma_r', 
                  vmin = np.nanpercentile(data_xr.min(dim = 'time')[dataset_id], 1), 
                  vmax = np.nanpercentile(data_xr.max(dim = 'time')[dataset_id], 99),
                  cbar = 1)

data_an = data_y - data_xr.mean(dim='time')
plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.1, vmax=.1, cbar = 1)

Mean Area

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'pH - MEAN AREA'},]

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));

Given point

loc = [7.37, 134.7]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'{label} at [{loc[0]}, {loc[1]}]'},]

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()

<matplotlib.legend.Legend at 0x17f6b0ec0>

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));